Abstract
As functional near-infrared spectroscopy (fNIRS) is developed as a neuroimaging technique and becomes an option to study a variety of populations and tasks, the reproducibility of the fNIRS signal is still subject of debate. By performing test–retest protocols over different functional tasks, several studies agree that the fNIRS signal is reproducible over group analysis, but the inter-subject and within-subject reproducibility is poor. The high variability at the first statistical level is often attributed to global systemic physiology. In the present work, we revisited the reproducibility of the fNIRS signal during a finger-tapping task across multiple sessions on the same and different days. We expanded on previous studies by hypothesizing that the lack of spatial information of the optodes contributes to the low reproducibility in fNIRS, and we incorporated a real-time neuronavigation protocol to provide accurate cortical localization of the optodes. Our proposed approach was validated in 10 healthy volunteers, and our results suggest that the addition of neuronavigation can increase the within-subject reproducibility of the fNIRS data, particularly in the region of interest. Unlike traditional approaches to positioning the optodes, in which low intra-subject reproducibility has been found, we were able to obtain consistent and robust activation of the contralateral primary motor cortex at the intra-subject level using a neuronavigation protocol. Overall, our findings support the hypothesis that at least part of the variability in fNIRS cannot be only attributed to global systemic physiology. The use of neuronavigation to guide probe positioning, as proposed in this work, has impacts to longitudinal protocols performed with fNIRS.
Highlights
IntroductionFunctional near-infrared spectroscopy (fNIRS) has emerged as a promising tool to measure brain function over the years (Ferrari and Quaresima, 2012; Boas et al, 2014; Strait and Scheutz, 2014; Herold et al, 2018; Quaresima and Ferrari, 2019). fNIRS relies on the fact that near-infrared light (∼700–900 nm) is absorbed by oxy- (HbO) and deoxy-hemoglobin (HbR), which allows one to Spatial Information Increases fNIRS Reproducibility estimate HbO/HbR concentration changes in biological tissue
Functional near-infrared spectroscopy has emerged as a promising tool to measure brain function over the years (Ferrari and Quaresima, 2012; Boas et al, 2014; Strait and Scheutz, 2014; Herold et al, 2018; Quaresima and Ferrari, 2019). fNIRS relies on the fact that near-infrared light (∼700–900 nm) is absorbed by oxy- (HbO) and deoxy-hemoglobin (HbR), which allows one to Spatial Information Increases fNIRS Reproducibility estimate HbO/HbR concentration changes in biological tissue
Participants performed a right-hand finger-tapping blockdesigned protocol consisting of 30 blocks of 2-s stimulation interleaved with a rest period that varied between 10 and 20 s. [Note that the rest period was randomized to decrease the probability of task synchronization with periodic physiological noise, such as Mayer waves (Yücel et al, 2016).] Since we wanted to investigate the effect of spatial information on the fNIRS reproducibility, we attempted to minimize the effects of circadian cycle in the measurements
Summary
Functional near-infrared spectroscopy (fNIRS) has emerged as a promising tool to measure brain function over the years (Ferrari and Quaresima, 2012; Boas et al, 2014; Strait and Scheutz, 2014; Herold et al, 2018; Quaresima and Ferrari, 2019). fNIRS relies on the fact that near-infrared light (∼700–900 nm) is absorbed by oxy- (HbO) and deoxy-hemoglobin (HbR), which allows one to Spatial Information Increases fNIRS Reproducibility estimate HbO/HbR concentration changes in biological tissue. Wearable technologies have expanded fNIRS protocols to study activities in natural and unconstrained environments (Piper et al, 2014; McKendrick et al, 2016; Pinti et al, 2018). These novel technologies afford new application scenarios never thought before, such as learning, training, and rehabilitation (Hatakenaka et al, 2007; Sitaram et al, 2009). Due to the increased interest of the neuroscience community in functional connectivity, several studies have attempted to characterize the reliability of restingstate networks extracted from the fNIRS signal during the resting state (Mesquita et al, 2010; Zhang et al, 2011; Niu et al, 2013; Novi et al, 2016). Due to the increased interest of the neuroscience community in functional connectivity, several studies have attempted to characterize the reliability of restingstate networks extracted from the fNIRS signal during the resting state (Mesquita et al, 2010; Zhang et al, 2011; Niu et al, 2013; Novi et al, 2016). Zhang et al (2011) concluded that fNIRS connectivity maps are highly robust at the group level and fairly reliable at the individual level. Niu et al (2013) and Novi et al (2016) independently showed that graph-theoretical approaches can extract reliable features from fNIRS data despite greater intraindividual variability in functional connectivity experiments
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